With numerous job opportunities and career opportunities in Kafka, the popularity of Apache Kafka is skyrocketing. Furthermore, in this day and age, knowing Kafka is a fast track to success.
So, in this article, “Most Popular Kafka Interview Questions and Answers,” we have compiled a list of the most frequently asked Apache Kafka Interview Questions and Answers for both experienced and inexperienced Kafka Technology professionals.
As a result, if you want to prepare for an Apache Kafka interview, this is the place to be. This will assist you in acing your Kafka interview.
Best kafka interview question and Answers:
Well here's a list of the most popular Kafka Interview Questions and Answers that any Interviewer may ask. So, continue reading until the end of the article “Kafka Interview Questions” to ace your interview on the first try.
Apache's response Kafka is an open source publish-subscribe message broker application. Scala was used to create this messaging application. This project was essentially initiated by the Apache software. Kafka's design pattern is primarily based on transactional logs.
The components of kafka are topic, producer, consumer and brokers.
The messages in the partitions are assigned a sequential ID number, which we refer to as an offset. So, we use these offsets to uniquely identify each message in the partition.
Apache Kafka invented the concept of Consumer Groups. Every Kafka consumer group is made up of one or more consumers who consume a set of subscribed topics together.
Apache's response Kafka is a distributed system designed to work with Zookeeper. However, Zookeeper's primary role in this context is to establish coordination between different nodes in a cluster. However, because it works as a periodically commit offset, we also use Zookeeper to recover from previously committed offsets if any node fails.
Because it is impossible to connect directly to the Kafka server without using Zookeeper, the answer is no. If ZooKeeper fails, it is impossible to service any client request.
There are only a few partitions available in every Kafka broker. And, in this case, each Kafka partition can be either a leader or a replica of a topic.
Apache Kafka has four main APIs:
Kafka Consumer primarily subscribes to a topic(s), as well as reads and processes messages from the topic (s). Furthermore, by naming a consumer group, consumers label themselves.
In other words, each record published to a topic is delivered to one consumer instance within each subscribing consumer group. Ascertain that Consumer instances can exist in separate processes or on separate machines.
It can perform in a variety of ways, including: >> To transmit data between two systems, we can build a real-time stream of data pipelines with it; >> We can also build a real-time streaming platform with Kafka that can actually react to the data.
The retention period, on the other hand, keeps all published records within the Kafka cluster. It makes no distinction between whether or not they have been consumed. Furthermore, the records can be discarded by configuring the retention period. And it has the added benefit of freeing up some space.
There are two basic methods of traditional message transfer, which are as follows:
Queuing is a method in which a pool of consumers reads a message from the server, and each message is delivered to one of them.
Publish-Subscribe: Messages are broadcast to all consumers in Publish-Subscribe.
Kafka has some advantages that make it worthwhile to use:
Because the Leader's primary role is to perform all read and write requests for the partition, Followers passively replicate the Leader.As a result, if the Leader fails, one of the Followers takes over as Leader. Essentially, this entire process ensures that the servers' load is balanced.
Replicas is essentially a list of nodes that replicate the log. Especially for a specific partition. They are, however, regardless of whether they play the role of Leader.
Furthermore, ISR stands for In-Sync Replicas. ISR is defined as a set of message replicas that are synced to the leaders.
We can be certain that published messages are not lost and can be consumed in the event of a machine error, a program error, or frequent software upgrades thanks to replication.
Simply put, it means that the Follower cannot retrieve data as quickly as the Leader.
When the Kafka Producer attempts to send messages at a rate that the Broker is unable to handle, a QueueFullException is typically thrown. However, because the Producer does not block, users will need to add enough brokers to collaboratively handle the increased load.
Producer API refers to an API that allows an application to publish a stream of records to one or more Kafka topics.
The main distinctions between Kafka and Flume are as follows:
Without a doubt, Kafka is a streaming platform. It can assist in the following ways:
To easily push records
Furthermore, it can store a large number of records without causing any storage issues.
Furthermore, it can process records as they arrive.
The maximum size of a message that Kafka can receive is approximately 1000000 bytes.
Streams API is an API that allows an application to act as a stream processor by consuming an input stream from one or more topics and producing an output stream to one or more output topics, as well as effectively transforming the input streams to output streams.
Producers' primary responsibility is to publish data on topics of their choosing. Its primary responsibility is to select the record to assign to the partition within the topic.
So, one way to tune Apache Kafka is to tune its various components:
Hence, you now know the best Kafka Interview Questions and Answers.
Furthermore, if you have recently attended any Kafka interviews, we would appreciate it if you could add more Kafka Interview Questions in the comments section. I hope this helps you get through the Kafka interview.
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